This invention relates generally to systems, and more particularly to a multivariable control system for manipulating process variables in the absence of a detailed a priori process model.
At least one known multivariable control technique uses a heuristic method of measuring responses to a step change of manipulated variables. Another known multivariable control technique uses a neural network expression of historical relationships of the measurements of manipulated variables. Yet another known multivariable control technique uses first principles representation of the characteristics of the process and its predicted reactions. Each of these known methods requires one or more of detailed data analysis, bump testing, or exact model development to accurately represent the multivariable control solution.
It would therefore be desirable to provide a multivariable control system that does not require detailed analysis and a priori model development., but instead is able to manipulate process variables based on changes in the controlled objectives as they occur during a process.
An exemplary embodiment of the present invention includes a multivariable control method for controlling a process characterized by a set of process variables and a set of controlled objectives. The multivariable control method manipulates the set of process variables based on observed changes in qualitative values of controlled objectives without extensive data analysis or a priori model development. The method includes the steps of operating the process at an initial set of process variables and an initial set of controlled objectives, monitoring the set of process variables and the set of controlled objectives while continuously operating the process, adjusting one or more members of the set of process variables based upon a non-linear optimization with respect to a desired set of controlled objectives, and utilizing the monitored set of process variables and the monitored set of controlled objectives in the non-linear optimization.
Thus, a set of desired controlled objectives can be maintained without a detailed model to predict the process performances.